Research on urban development and security in border areas of China based on deep learning

doi:10.6046/zrzyyg.2021157

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Abstract

In order to explore the development trend of border cities in China and assess the city’s border defense capability, the D-LinkNet34 deep learning algorithm is used to automate the extraction of buildings and roads in Tuolin, Shiquanhe and Pulan towns in Tibet Autonomous Region, and to analyze the development trend and border defense capability of border towns based on landscape index and population size. Analysis results show that: ① The extraction method based on D-LinkNet deep learning network can effectively further classify urban construction land, with average total progress of more than 80% and IOU above 70%.② The distribution of plaques in the towns of Pulan and Shiquanhe shows a trend of aggregation, and the trend of urban expansion weakened. The distribution of plaques in Tuolin Town shows a scattered trend, and the trend of urban expansion is obvious. ③ The building area is linearly related to the resident population, and the building area of Tuolin Town increased by about 68.75%from 2002 to 2018, and the resident population increased by about 39.00%. The building area of Shiquanhe Town increased by about 70.75% from 2004 to 2020, while the resident population increased by about 68.44%. The building area of Pulan Town increased by about 68.36% from 2005 to 2018, while the resident population increased by about 25.04%. This study provides a new method for quantitative evaluation of the expansion characteristics and border defense capability of border cities, as well as a reference for building China’s border defense capability.

Keywords remote sensing border towns urban development landscape index D-LinkNet

ZTFLH:

TP79

Corresponding Authors: LIU Jilei E-mail: 505474279@qq.com;liujilei@ppsuc.edu.cn

Issue Date: 20 June 2022

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	Xiaoyu MA
	Xin ZHANG
	Jilei LIU
	Nan ZHOU
	Kejian LIU
	Chunshan WEI
	Peng YANG

Cite this article:

Xiaoyu MA,Xin ZHANG,Jilei LIU, et al. Research on urban development and security in border areas of China based on deep learning[J]. Remote Sensing for Natural Resources, 2022, 34(2): 231-241.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021157 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/231

Fig.1 Technical flow chart

Fig.2 D-LinkNet34 network structure

Fig.3 Training extraction flowchart

Tab.1 Classification of urban landscape index types

Fig.4 Comparison of classification results of Shiquanhe Town in 2020

Fig.5 Comparison of classification results of Tuolin Town and Pulan Town in 2018

Tab.2 Evaluation of classification accuracy

Fig.6 Changes of patch type index in different periods in 3 townships

Fig.7 Development rate of buildings and roads in 3 towns

Tab.3 Visualization of architectural changes in 3 townships in different periods

Tab.4 Visualization of road changes in 3 townships in different periods

Fig.8 Changes in the landscape level index of 3 townships in different periods

Fig.9 Visualization of landscape changes in different periods in Tuolin Town

Fig.10 Visualization of landscape changes in different periods in Shiquanhe Town

Fig.11 Visualization of landscape changes in different periods in Pulan Town

Tab.5 Changes in border security capabilities of 3 towns

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